Framework overview.
One straightforward way to realize the class conditional generation is to use an existing conditional generative adversarial network (cGAN [1]}), which can generate images that are highly associated with the given condition.
However, as existing cGAN techniques generate images from a randomly sampled latent vector \(\mathbf {z}\)  from Gaussian distribution, the generated image is not directly related to the input \(\mathbf {x}\) . Consequently, they can be very different from \(\mathbf {x}\)  even under the correct condition label (e.g., the input can be "red bird flying in the blue sky" while the generated image can be "yellow bird standing on the green tree").
